Bearing Capacity Evaluation of Tunnel-Type Anchorage Based on Artificial Intelligent Algorithm

WANG Zhonghao; GUO Xifeng; YANG Xingyu

doi:10.3969/j.issn.0258-2724.20200165

Volume 56 Issue 3

Jun. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(3): 534-540.

WANG Zhonghao, GUO Xifeng, YANG Xingyu. Bearing Capacity Evaluation of Tunnel-Type Anchorage Based on Artificial Intelligent Algorithm[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 534-540. doi: 10.3969/j.issn.0258-2724.20200165

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WANG Zhonghao, GUO Xifeng, YANG Xingyu. Bearing Capacity Evaluation of Tunnel-Type Anchorage Based on Artificial Intelligent Algorithm[J]. Journal of Southwest Jiaotong University, 2021, 56(3): 534-540. doi: 10.3969/j.issn.0258-2724.20200165

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Bearing Capacity Evaluation of Tunnel-Type Anchorage Based on Artificial Intelligent Algorithm

doi: 10.3969/j.issn.0258-2724.20200165

Received Date: 06 Apr 2020
Rev Recd Date: 11 Jun 2020

Available Online: 08 Feb 2021

Publish Date: 15 Jun 2021

Abstract

Abstract

At present, the tunnel-type anchorage is short of reasonable analytical formula for the evaluation of the bearing capacity, the model test is time-consuming and labor-consuming, and its numerical simulation has poor reliability. To handle the above problems, an artificial intelligence method is presented for predicting the bearing capacity of the tunnel-type anchorage. Starting from its force transmission process, the factors influencing the bearing capacity are analyzed and the evaluation index system of bearing capacity has been determined. Then, given the strong learning prediction ability of least squares support vector machines (LSSVM) and excellent performance of particle swarm optimization (PSO), a PSO-LSSVM model with nonlinear mapping of bearing capacity is established. After training the model with 17 cases of the tunnel-type anchorage as input samples, the optimal combination of kernel parameters and penalty coefficients is determined to be (1,500). Finally, the model is used to predict the bearing capacity of a bridge tunnel-type anchorage and the prediction result is determined as 10.2P. The comparison with the bearing capacity result of 11.0P that is determined by the comprehensive study of the scale model test and numerical simulation method, demonstrate that the predicted result is slightly lower but very close to the result of other method. This also shows that the prediction results of the model are reasonable, reliable and conservative, and the prediction effect is desirable.
- tunnel-type anchorage,
- bearing capacity,
- least squares support vector machines (LSSVM),
- particle swarm optimization (PSO)

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References(13)

References

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